Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement

Abel D, Agarwal A, Diaz F, Krishnamurthy A, Schapire RE (2016) Exploratory gradient boosting for reinforcement learning in complex domains. arXiv preprint arXiv:1603.04119 Adams S, Arel I, Bach J, Coop R, Furlan R, Goertzel B, Hall JS, Samsonovich A, Scheutz M, Schlesinger M, Shapiro SC, Sowa J (2012) Mapping the landscape of human-level artificial general intelligence. AI Mag 33(1):25–42 Adams SS, Banavar G, Campbell M (2016) I-athlon: towards a multi-dimensional Turing test. AI Mag 37(1):78–84 Alcalá J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2010) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Logic Soft Comput 17:255–287 Alexander JRM, Smales S (1997) Intelligence, learning and long-term memory. Personal Individ Differ 23(5):815–825 Alpcan T, Everitt T, Hutter M (2014) Can we measure the difficulty of an optimization problem? In: IEEE information theory workshop (ITW) Alur R, Bodik R, Juniwal G, Martin MMK, Raghothaman M, Seshia SA, Singh R, Solar-Lezama A, Torlak E, Udupa A (2013) Syntax-guided synthesis. In: Formal methods in computer-aided design (FMCAD), 2013, IEEE, pp 1–17 Alvarado N, Adams SS, Burbeck S, Latta C (2002) Beyond the Turing test: performance metrics for evaluating a computer simulation of the human mind. In: Proceedings of the 2nd international conference on development and learning, IEEE, pp 147–152 Amigoni F, Bastianelli E, Berghofer J, Bonarini A, Fontana G, Hochgeschwender N, Iocchi L, Kraetzschmar G, Lima P, Matteucci M, Miraldo P, Nardi D, Schiaffonati V (2015) Competitions for benchmarking: task and functionality scoring complete performance assessment. IEEE Robot Autom Mag 22(3):53–61 Anderson J, Lebiere C (2003) The Newell test for a theory of cognition. Behav Brain Sci 26(5):587–601 Anderson J, Baltes J, Cheng CT (2011) Robotics competitions as benchmarks for AI research. Knowl Eng Rev 26(01):11–17 Arel I, Rose DC, Karnowski TP (2010) Deep machine learning—a new frontier in artificial intelligence research. IEEE Comput Intell Mag 5(4):13–18 Asada M, Hosoda K, Kuniyoshi Y, Ishiguro H, Inui T, Yoshikawa Y, Ogino M, Yoshida C (2009) Cognitive developmental robotics: a survey. IEEE Trans Auton Ment Dev 1(1):12–34 Aziz H, Brill M, Fischer F, Harrenstein P, Lang J, Seedig HG (2015) Possible and necessary winners of partial tournaments. J Artif Intell Res 54:493–534 Bache K, Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml Bagnall AJ, Zatuchna ZV (2005) On the classification of maze problems. In: Bull L, Kovacs T (eds) Foundations of learning classifier system. Studies in fuzziness and soft computing, vol. 183, Springer, pp 305–316. http://rd.springer.com/chapter/10.1007/11319122_12 Baldwin D, Yadav SB (1995) The process of research investigations in artificial intelligence - a unified view. IEEE Trans Syst Man Cybern 25(5):852–861 Bellemare MG, Naddaf Y, Veness J, Bowling M (2013) The arcade learning environment: an evaluation platform for general agents. J Artif Intell Res 47:253–279 Besold TR (2014) A note on chances and limitations of psychometric ai. In: KI 2014: advances in artificial intelligence. Springer, pp 49–54 Biever C (2011) Ultimate IQ: one test to rule them all. New Sci 211(2829, 10 September 2011):42–45 Borg M, Johansen SS, Thomsen DL, Kraus M (2012) Practical implementation of a graphics Turing test. In: Advances in visual computing. Springer, pp 305–313 Boring EG (1923) Intelligence as the tests test it. New Repub 35–37 Bostrom N (2014) Superintelligence: paths, dangers, strategies. Oxford University Press, Oxford Brazdil P, Carrier CG, Soares C, Vilalta R (2008) Metalearning: applications to data mining. Springer, New York Bringsjord S (2011) Psychometric artificial intelligence. J Exp Theor Artif Intell 23(3):271–277 Bringsjord S, Schimanski B (2003) What is artificial intelligence? Psychometric AI as an answer. In: International joint conference on artificial intelligence, pp 887–893 Brundage M (2016) Modeling progress in ai. AAAI 2016 Workshop on AI, Ethics, and Society Buchanan BG (1988) Artificial intelligence as an experimental science. Springer, New York Buhrmester M, Kwang T, Gosling SD (2011) Amazon’s mechanical turk a new source of inexpensive, yet high-quality, data? Perspect Psychol Sci 6(1):3–5 Bursztein E, Aigrain J, Moscicki A, Mitchell JC (2014) The end is nigh: generic solving of text-based captchas. In: Proceedings of the 8th USENIX conference on Offensive Technologies, USENIX Association, p 3 Campbell M, Hoane AJ, Hsu F (2002) Deep Blue. Artif Intell 134(1–2):57–83 Cangelosi A, Schlesinger M, Smith LB (2015) Developmental robotics: from babies to robots. MIT Press, Cambridge Caputo B, Müller H, Martinez-Gomez J, Villegas M, Acar B, Patricia N, Marvasti N, Üsküdarlı S, Paredes R, Cazorla M et al (2014) Imageclef 2014: overview and analysis of the results. In: Information access evaluation. Multilinguality, multimodality, and interaction, Springer, pp 192–211 Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka ER Jr, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: AAAI, vol 5, p 3 Carroll JB (1993) Human cognitive abilities: a survey of factor-analytic studies. Cambridge University Press, Cambridge Caruana R (1997) Multitask learning. Mach Learn 28(1):41–75 Chaitin GJ (1982) Gödel’s theorem and information. Int J Theor Phys 21(12):941–954 Chandrasekaran B (1990) What kind of information processing is intelligence? In: The foundation of artificial intelligence—a sourcebook. Cambridge University Press, pp 14–46 Chater N (1999) The search for simplicity: a fundamental cognitive principle? Q J Exp Psychol Sect A 52(2):273–302 Chater N, Vitányi P (2003) Simplicity: a unifying principle in cognitive science? Trends Cogn Sci 7(1):19–22 Chu Z, Gianvecchio S, Wang H, Jajodia S (2010) Who is tweeting on twitter: human, bot, or cyborg? In: Proceedings of the 26th annual computer security applications conference, ACM, pp 21–30 Cochran WG (2007) Sampling techniques. Wiley, New York Cohen PR, Howe AE (1988) How evaluation guides AI research: the message still counts more than the medium. AI Mag 9(4):35 Cohen Y (2013) Testing and cognitive enhancement. Technical repor, National Institute for Testing and Evaluation, Jerusalem, Israel Conrad JG, Zeleznikow J (2013) The significance of evaluation in AI and law: a case study re-examining ICAIL proceedings. In: Proceedings of the 14th international conference on artificial intelligence and law, ACM, pp 186–191 Conrad JG, Zeleznikow J (2015) The role of evaluation in ai and law. In: Proceedings of the 15th international conference on artificial intelligence and law, pp 181–186 Deary IJ, Der G, Ford G (2001) Reaction times and intelligence differences: a population-based cohort study. Intelligence 29(5):389–399 Decker KS, Durfee EH, Lesser VR (1989) Evaluating research in cooperative distributed problem solving. Distrib Artif Intell 2:487–519 Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30 Detterman DK (2011) A challenge to Watson. Intelligence 39(2–3):77–78 Dimitrakakis C (2016) Personal communication Dimitrakakis C, Li G, Tziortziotis N (2014) The reinforcement learning competition 2014. AI Mag 35(3):61–65 Dowe DL (2013) Introduction to Ray Solomonoff 85th memorial conference. In: Dowe DL (ed) Algorithmic probability and friends. Bayesian prediction and artificial intelligence, lecture notes in computer science, vol 7070. Springer, Berlin, pp 1–36 Dowe DL, Hajek AR (1997) A computational extension to the Turing Test. In: Proceedings of the 4th conference of the Australasian cognitive science society, University of Newcastle, NSW, Australia Dowe DL, Hajek AR (1998) A non-behavioural, computational extension to the Turing test. In: International conference on computational intelligence and multimedia applications (ICCIMA’98), Gippsland, Australia, pp 101–106 Dowe DL, Hernández-Orallo J (2012) IQ tests are not for machines, yet. Intelligence 40(2):77–81 Dowe DL, Hernández-Orallo J (2014) How universal can an intelligence test be? Adapt Behav 22(1):51–69 Drummond C (2009) Replicability is not reproducibility: nor is it good science. In: Proceedings of the evaluation methods for machine learning workshop at the 26th ICML, Montreal, Canada Drummond C, Japkowicz N (2010) Warning: statistical benchmarking is addictive. Kicking the habit in machine learning. J Exp Theor Artif Intell 22(1):67–80 Duan Y, Chen X, Houthooft R, Schulman J, Abbeel P (2016) Benchmarking deep reinforcement learning for continuous control. arXiv preprint arXiv:1604.06778 Eden AH, Moor JH, Soraker JH, Steinhart E (2013) Singularity hypotheses: a scientific and philosophical assessment. Springer, New York Edmondson W (2012) The intelligence in ETI—what can we know? Acta Astronaut 78:37–42 Elo AE (1978) The rating of chessplayers, past and present, vol 3. Batsford, London Embretson SE, Reise SP (2000) Item response theory for psychologists. L. Erlbaum, Hillsdale Evans JM, Messina ER (2001) Performance metrics for intelligent systems. NIST Special Publication SP, pp 101–104 Everitt T, Lattimore T, Hutter M (2014) Free lunch for optimisation under the universal distribution. In: 2014 IEEE Congress on evolutionary computation (CEC), IEEE, pp 167–174 Falkenauer E (1998) On method overfitting. J Heuristics 4(3):281–287 Feldman J (2003) Simplicity and complexity in human concept learning. Gen Psychol 38(1):9–15 Ferrando PJ (2009) Difficulty, discrimination, and information indices in the linear factor analysis model for continuous item responses. Appl Psychol Meas 33(1):9–24 Ferrando PJ (2012) Assessing the discriminating power of item and test scores in the linear factor-analysis model. Psicológica 33:111–139 Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recogn Lett 30(1):27–38 Ferrucci D, Brown E, Chu-Carroll J, Fan J, Gondek D, Kalyanpur AA, Lally A, Murdock J, Nyberg E, Prager J et al (2010) Building Watson: an overview of the DeepQA project. AI Mag 31(3):59–79 Fogel DB (1991) The evolution of intelligent decision making in gaming. Cybern Syst 22(2):223–236 Gaschnig J, Klahr P, Pople H, Shortliffe E, Terry A (1983) Evaluation of expert systems: issues and case studies. Build Exp Syst 1:241–278 Geissman JR, Schultz RD (1988) Verification & validation. AI Exp 3(2):26–33 Genesereth M, Love N, Pell B (2005) General game playing: overview of the AAAI competition. AI Mag 26(2):62 Gerónimo D, López AM (2014) Datasets and benchmarking. In: Vision-based pedestrian protection systems for intelligent vehicles. Springer, pp 87–93 Goertzel B, Pennachin C (eds) (2007) Artificial general intelligence. Springer, New York Goertzel B, Arel I, Scheutz M (2009) Toward a roadmap for human-level artificial general intelligence: embedding HLAI systems in broad, approachable, physical or virtual contexts. Artif Gen Intell Roadmap Initiat Goldreich O, Vadhan S (2007) Special issue on worst-case versus average-case complexity editors’ foreword. Comput complex 16(4):325–330 Gordon BB (2007) Report on panel discussion on (re-)establishing or increasing collaborative links between artificial intelligence and intelligent systems. In: Messina ER, Madhavan R (eds) Proceedings of the 2007 workshop on performance metrics for intelligent systems, pp 302–303 Gulwani S, Hernández-Orallo J, Kitzelmann E, Muggleton SH, Schmid U, Zorn B (2015) Inductive programming meets the real world. Commun ACM 58(11):90–99 Hand DJ (2004) Measurement theory and practice. A Hodder Arnold Publication, London Hernández-Orallo J (2000a) Beyond the Turing test. J Logic Lang Inf 9(4):447–466 Hernández-Orallo J (2000b) On the computational measurement of intelligence factors. In: Meystel A (ed) Performance metrics for intelligent systems workshop. National Institute of Standards and Technology, Gaithersburg, pp 1–8 Hernández-Orallo J (2000c) Thesis: computational measures of information gain and reinforcement in inference processes. AI Commun 13(1):49–50 Hernández-Orallo J (2010) A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In: Artificial general intelligence, 3rd International Conference. Atlantis Press, Extended report at http://users.dsic.upv.es/proy/anynt/unbiased.pdf, pp 182–183 Hernández-Orallo J (2014) On environment difficulty and discriminating power. Auton Agents Multi-Agent Syst. 29(3):402–454. doi:10.1007/s10458-014-9257-1 Hernández-Orallo J, Dowe DL (2010) Measuring universal intelligence: towards an anytime intelligence test. Artif Intell 174(18):1508–1539 Hernández-Orallo J, Dowe DL (2013) On potential cognitive abilities in the machine kingdom. Minds Mach 23:179–210 Hernández-Orallo J, Minaya-Collado N (1998) A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In: Proceedings of international symposium of engineering of intelligent systems (EIS’98), ICSC Press, pp 146–163 Hernández-Orallo J, Dowe DL, España-Cubillo S, Hernández-Lloreda MV, Insa-Cabrera J (2011) On more realistic environment distributions for defining, evaluating and developing intelligence. In: Schmidhuber J, Thórisson K, Looks M (eds) Artificial general intelligence, LNAI, vol 6830. Springer, New York, pp 82–91 Hernández-Orallo J, Flach P, Ferri C (2012a) A unified view of performance metrics: translating threshold choice into expected classification loss. J Mach Learn Res 13(1):2813–2869 Hernández-Orallo J, Insa-Cabrera J, Dowe DL, Hibbard B (2012b) Turing Tests with Turing machines. In: Voronkov A (ed) Turing-100, EPiC Series, vol 10, pp 140–156 Hernández-Orallo J, Dowe DL, Hernández-Lloreda MV (2014) Universal psychometrics: measuring cognitive abilities in the machine kingdom. Cogn Syst Res 27:50–74 Hernández-Orallo J, Martínez-Plumed F, Schmid U, Siebers M, Dowe DL (2016) Computer models solving intelligence test problems: progress and implications. Artif Intell 230:74–107 Herrmann E, Call J, Hernández-Lloreda MV, Hare B, Tomasello M (2007) Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis. Science 317(5843):1360–1366 Hibbard B (2009) Bias and no free lunch in formal measures of intelligence. J Artif Gen Intell 1(1):54–61 Hingston P (2010) A new design for a Turing Test for bots. In: 2010 IEEE symposium on computational intelligence and games (CIG), IEEE, pp 345–350 Hingston P (2012) Believable bots: can computers play like people?. Springer, New York Ho TK, Basu M (2002) Complexity measures of supervised classification problems. IEEE Trans Pattern Anal Mach Intell 24(3):289–300 Hutter M (2007) Universal algorithmic intelligence: a mathematical top\(\rightarrow \)down approach. In: Goertzel B, Pennachin C (eds) Artificial general intelligence, cognitive technologies. Springer, Berlin, pp 227–290 Igel C, Toussaint M (2005) A no-free-lunch theorem for non-uniform distributions of target functions. J Math Model Algorithms 3(4):313–322 Insa-Cabrera J (2016) Towards a universal test of social intelligence. Ph.D. thesis, Departament de Sistemes Informátics i Computació, UPV Insa-Cabrera J, Dowe DL, España-Cubillo S, Hernández-Lloreda MV, Hernández-Orallo J (2011a) Comparing humans and ai agents. In: Schmidhuber J, Thórisson K, Looks M (eds) Artificial general intelligence, LNAI, vol 6830. Springer, New York, pp 122–132 Insa-Cabrera J, Dowe DL, Hernández-Orallo J (2011) Evaluating a reinforcement learning algorithm with a general intelligence test. In: Lozano JA, Gamez JM (eds) Current topics in artificial intelligence. CAEPIA 2011, LNAI series 7023. Springer, New York Insa-Cabrera J, Benacloch-Ayuso JL, Hernández-Orallo J (2012) On measuring social intelligence: experiments on competition and cooperation. In: Bach J, Goertzel B, Iklé M (eds) AGI, lecture notes in computer science, vol 7716. Springer, New York, pp 126–135 Jacoff A, Messina E, Weiss BA, Tadokoro S, Nakagawa Y (2003) Test arenas and performance metrics for urban search and rescue robots. In: Proceedings of 2003 IEEE/RSJ international conference on intelligent robots and systems, 2003 (IROS 2003), IEEE, vol 4, pp 3396–3403 Japkowicz N, Shah M (2011) Evaluating learning algorithms. Cambridge University Press, Cambridge Jiang J (2008) A literature survey on domain adaptation of statistical classifiers. http://sifaka.cs.uiuc.edu/jiang4/domain_adaptation/survey Johnson M, Hofmann K, Hutton T, Bignell D (2016) The Malmo platform for artificial intelligence experimentation. In: International joint conference on artificial intelligence (IJCAI) Keith TZ, Reynolds MR (2010) Cattell–Horn–Carroll abilities and cognitive tests: what we’ve learned from 20 years of research. Psychol Schools 47(7):635–650 Ketter W, Symeonidis A (2012) Competitive benchmarking: lessons learned from the trading agent competition. AI Mag 33(2):103 Khreich W, Granger E, Miri A, Sabourin R (2012) A survey of techniques for incremental learning of HMM parameters. Inf Sci 197:105–130 Kim JH (2004) Soccer robotics, vol 11. Springer, New York Kitano H, Asada M, Kuniyoshi Y, Noda I, Osawa E (1997) Robocup: the robot world cup initiative. In: Proceedings of the first international conference on autonomous agents, ACM, pp 340–347 Kleiner K (2011) Who are you calling bird-brained? An attempt is being made to devise a universal intelligence test. Economist 398(8723, 5 March 2011):82 Knuth DE (1973) Sorting and searching, volume 3 of the art of computer programming. Addison-Wesley, Reading Koza JR (2010) Human-competitive results produced by genetic programming. Genet Program Evolvable Mach 11(3–4):251–284 Krueger J, Osherson D (1980) On the psychology of structural simplicity. In: Jusczyk PW, Klein RM (eds) The nature of thought: essays in honor of D. O. Hebb. Psychology Press, London, pp 187–205 Langford J (2005) Clever methods of overfitting. Machine Learning (Theory). http://hunch.net Langley P (1987) Research papers in machine learning. Mach Learn 2(3):195–198 Langley P (2011) The changing science of machine learning. Mach Learn 82(3):275–279 Langley P (2012) The cognitive systems paradigm. Adv Cogn Syst 1:3–13 Lattimore T, Hutter M (2013) No free lunch versus Occam’s razor in supervised learning. Algorithmic Probability and Friends. Springer, Bayesian Prediction and Artificial Intelligence, pp 223–235 Leeuwenberg ELJ, Van Der Helm PA (2012) Structural information theory: the simplicity of visual form. Cambridge University Press, Cambridge Legg S, Hutter M (2007a) Tests of machine intelligence. In: Lungarella M, Iida F, Bongard J, Pfeifer R (eds) 50 Years of Artificial Intelligence, Lecture Notes in Computer Science, vol 4850, Springer Berlin Heidelberg, pp 232–242. doi:10.1007/978-3-540-77296-5_22 Legg S, Hutter M (2007b) Universal intelligence: a definition of machine intelligence. Minds Mach 17(4):391–444 Legg S, Veness J (2013) An approximation of the universal intelligence measure. Algorithmic Probability and Friends. Springer, Bayesian Prediction and Artificial Intelligence, pp 236–249 Levesque HJ (2014) On our best behaviour. Artif Intell 212:27–35 Levesque HJ, Davis E, Morgenstern L (2012) The winograd schema challenge. In: Proceedings of the thirteenth international conference on the principles of knowledge representation and reasoning, pp 552–561 Levin LA (1973) Universal sequential search problems. Prob Inf Transm 9(3):265–266 Levin LA (1986) Average case complete problems. SIAM J Comput 15:285–286 Levin LA (2013) Universal heuristics: how do humans solve unsolvable problems? In: Dowe DL (ed) Algorithmic probability and friends. Bayesian prediction and artificial intelligence, lecture notes in computer science, vol 7070. Springer, New York, pp 53–54 Li M, Vitányi P (2008) An introduction to Kolmogorov complexity and its applications, 3rd edn. Springer, New York Livingstone D (2006) Turing’s test and believable AI in games. Comput Entertain CIE 4(1):6 Llargues-Asensio JM, Peralta J, Arrabales R, González-Bedía M, Cortez P, López-Peña AL (2014) Artificial intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters. Expert Systems with Applications Long D, Fox M (2003) The 3rd international planning competition: results and analysis. J Artif Intell Res JAIR 20:1–59 Lord FM (1980) Applications of item response theory to practical testing problems. Erlbaum, Mahwah Macià N, Bernadó-Mansilla E (2014) Towards UCI+: a mindful repository design. Inf Sci 261:237–262 Madhavan R, Tunstel E, Messina E (2009) Performance evaluation and benchmarking of intelligent systems. Springer, New York Mahoney MV (1999) Text compression as a test for artificial intelligence. In: Proceedings of the national conference on artificial intelligence, AAAI, p 970 Marché C, Zantema H (2007) The termination competition. In: Term rewriting and applications, Springer, pp 303–313 Marcus G, Rossi F, Veloso M (2016) Beyond the Turing test (special issue). AI Mag 37(1):3–101 Masum H, Christensen S (2003) The turing ratio: a framework for open-ended task metrics. J Evol Technol Masum H, Christensen S, Oppacher F (2002) The turing ratio: metrics for open-ended tasks. In: GECCO, Citeseer, pp 973–980 McCarthy J (2007) What is artificial intelligence. Technical report, Stanford University. http://www-formal.stanford.edu/jmc/whatisai.html McCorduck P (2004) Machines who think. A K Peters/CRC Press, Boca Raton McDermott J, White DR, Luke S, Manzoni L, Castelli M, Vanneschi L, Jaśkowski W, Krawiec K, Harper R, Jong KD, O’Reilly UM (2012) Genetic programming needs better benchmarks. In: Proceedings of the 14th international conference on Genetic and evolutionary computation conference. ACM, Philadelphia, pp 791–798 McGuigan M (2006) Graphics Turing Test. arXiv preprint arXiv:cs/0603132 Melkikh AV (2014) The no free lunch theorem and hypothesis of instinctive animal behavior. Artif Intell Res 3(4):p43 Mellenbergh GJ (1994) Generalized linear item response theory. Psychol Bull 115(2):300 Mesnil G, Dauphin Y, Glorot X, Rifai S, Bengio Y, Goodfellow IJ, Lavoie E, Muller X, Desjardins G, Warde-Farley D, et al (2012) Unsupervised and transfer learning challenge: a deep learning approach. JMLR: Workshop and Conference Proceedings, 2012 ICML Workshop on Unsupervised and Transfer Learning vol 27, pp 97–110 Messina E, Meystel A, Reeker L (2001) PerMIS 2001, white paper. In: Meystel AM, Messina ER (eds) Measuring the performance and intelligence of systems: proceedings of the 2001 PerMIS Workshop, September 4, 2001, National Institute of Standards and Technology (NIST) Special Publication 982. Gaithersburg, pp 3–15 Meystel A (2000) Permis 2000 white paper: measuring performance and intelligence of systems with autonomy. In: Meystel AM, Messina ER (eds) Measuring the performance and intelligence of systems: proceedings of the 2000 PerMIS Workshop, August 14–16, 2000, National Institute of Standards and Technology (NIST) Special Publication 970. Gaithersburg, pp 1–34 Meystel A, Albus J, Messina E, Leedom D (2003a) Performance measures for intelligent systems: measures of technology readiness. Technical report, DTIC Document Meystel A, Albus J, Messina E, Leedom D (2003) Permis 2003 white paper: performance measures for intelligent systems—measures of technology readiness. In: Meystel AM, Messina ER (eds) Measuring the performance and intelligence of systems: proceedings of the 2003 PerMIS Workshop, National Institute of Standards and Technology (NIST) Special Publication 1014. Gaithersburg Minsky ML (ed) (1968) Semantic information processing. MIT Press, Cambridge Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533 Morgenstern L, Davis E, Ortiz-Jr CL (2016) Planning, executing, and evaluating the Winograd schema challenge. AI Mag 37(1):50–54 Mueller S, Jones M, Minnery B, Hiland JM (2007) The bica cognitive decathlon: a test suite for biologically-inspired cognitive agents. In: Proceedings of behavior representation in modeling and simulation conference, Norfolk Mueller ST (2010) A partial implementation of the BICA cognitive decathlon using the psychology experiment building language (PEBL). Int J Mach Conscious 2(02):273–288 Mueller ST, Minnery BS (2008) Adapting the Turing Test for embodied neurocognitive evaluation of biologically-inspired cognitive agents. In: Proceedings of 2008 AAAI fall symposium on biologically inspired cognitive architectures Newell A (1973) You can’t play 20 questions with nature and win: projective comments on the papers of this symposium. In: Chase W (ed) Vis Inf Process. Academic Press, New York, pp 283–308 Newell A (1980) Physical symbol systems. Cogn Sci 4(2):135–183 Newell A (1990) Unified theories of cognition. Harvard University, Cambridge Newell A, Simon HA (1976) Computer science as empirical inquiry: symbols and search. Commun ACM 19(3):113–126 Nizamani AR (2015) Reasoning with bounded cognitive resources. Ph.D. thesis, Department of Applied Information Technology, Chalmers University of Technology & University of Gothenburg, Sweden Oppy G, Dowe DL (2011) The Turing Test. In: Zalta EN (ed) Stanford Encyclopedia of Philosophy, Stanford University. http://plato.stanford.edu/entries/turing-test/ Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359 Perez D, Samothrakis S, Togelius J, Schaul T, Lucas S, Couëtoux A, Lee J, Lim CU, Thompson T (2015) The 2014 general video game playing competition. IEEE Transactions on Computational Intelligence and AI in Games Potthast M, Hagen M, Gollub T, Tippmann M, Kiesel J, Rosso P, Stamatatos E, Stein B (2013) Overview of the 5th international competition on plagiarism detection. CLEF (2013) Evaluation labs and workshop working notes papers, pp 23–26 September. Valencia, Spain Proudfoot D (2011) Anthropomorphism and AI: Turing’s much misunderstood imitation game. Artif Intell 175(5):950–957 Quinn AJ, Bederson BB (2011) Human computation: a survey and taxonomy of a growing field. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp 1403–1412 Rajani S (2011) Artificial intelligence—man or machine. Int J Inf Technol 4(1):173–176 Rao RB, Fung G, Rosales R (2008) On the dangers of cross-validation. an experimental evaluation. In: SDM, SIAM, pp 588–596 Rohrer B (2010) Accelerating progress in artificial general intelligence: choosing a benchmark for natural world interaction. J Artif Gen Intell 2(1):1–28 Rothenberg J, Paul J, Kameny I, Kipps JR, Swenson M (1987) Evaluating expert system tools: a framework and methodology-workshops. Technical report, DTIC Document Russell S, Norvig P (2009) Artificial intelligence: a modern approach. Prentice Hall, Upper Saddle River Sanghi P, Dowe DL (2003) A computer program capable of passing IQ tests. In: 4th international conference on cognitive science (ICCS’03), Sydney, pp 570–575 Schaeffer J, Burch N, Bjornsson Y, Kishimoto A, Muller M, Lake R, Lu P, Sutphen S (2007) Checkers is solved. Science 317(5844):1518 Schaie KW (2010) Primary mental abilities. Corsini Encyclopedia of Psychology Schaul T (2014) An extensible description language for video games. IEEE Trans Comput Intell AI Games PP(99):1–1. doi:10.1109/TCIAIG.2014.2352795 Schenck C (2013) Intelligence tests for robots: Solving perceptual reasoning tasks with a humanoid robot. Master’s thesis, Iowa State University Schlenoff C, Scott H, Balakirsky S (2011) Performance evaluation of intelligent systems at the National Institute of Standards and Technology (NIST). Technical report, DTIC Document Schmid U, Ragni M (2015) Comparing computer models solving number series problems. In: Artificial general intelligence. Springer, pp 352–361 Schweizer P (1998) The truly total Turing test. Minds Mach 8(2):263–272 Searle JR (1980) Minds, brains, and programs. Behav Brain Sci 3:417–457 Seber GAF, Salehi MM (2013) Adaptive cluster sampling. In: Adaptive sampling designs. Springer, pp 11–26 Settles B (2012) Active learning. Synth Lect Artif Intell Mach Learn 6(1):1–114 Shettleworth SJ (2010) Cognition, evolution, and behavior. Oxford University Press, Oxford Shettleworth SJ, Bloom P, Nadel L (2013) Fundamentals of comparative cognition. Oxford University Press, Oxford Shieber SM (2016) Principles for designing an AI competition, or why the Turing test fails as an inducement prize. AI Mag 37(1):91–96 Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484–489 Simmons R (2000) Survivability and competence as measures of intelligent systems. In: Meystel AM, Messina ER (eds) Measuring the performance and intelligence of systems: proceedings of the 2000 PerMIS Workshop, August 14–16, 2000, National Institute of Standards and Technology (NIST) Special Publication 970. Gaithersburg, pp 162–163 Simon HA (1995) Artificial intelligence: an empirical science. Artif Intell 77(1):95–127 Sloman A, Scheutz M (2002) A framework for comparing agent architectures. Proceedings of UKCI 2 Smith WD (2002) Rating systems for gameplayers, and learning. NEC, Princeton, NJ, Technical report, pp 93–104 Smith WD (2006) Mathematical definition of “intelligence” (and consequences). Unpublished report Soares C (2009) UCI++: improved support for algorithm selection using datasetoids. In: Advances in knowledge discovery and data mining. Springer, pp 499–506 Solomonoff R (1996) Does algorithmic probability solve the problem of induction. Inf Stat Induction Sci 7–8 Solomonoff RJ (1964) A formal theory of inductive inference. Part I. Inf Control 7(1):1–22 Solomonoff RJ (1984) Optimum sequential search. Oxbridge Research, Cambridge. http://world.std.com/~rjs/optseq.pdf Srinivasan R (2002) Importance sampling: applications in communications and detection. Springer, New York Starkie B, van Zaanen M, Estival D (2006) The Tenjinno machine translation competition. In: Grammatical inference: algorithms and applications. Springer, pp 214–226 Sternberg RJ (ed) (2000) Handbook of intelligence. Cambridge University Press, Cambridge Strannegård C, Amirghasemi M, Ulfsbücker S (2013a) An anthropomorphic method for number sequence problems. Cogn Syst Res 22–23:27–34 Strannegård C, Nizamani A, Sjöberg A, Engström F (2013b) Bounded Kolmogorov complexity based on cognitive models. In: Kühnberger KU, Rudolph S, Wang P (eds) Artificial general intelligence. Lecture notes in computer science, vol 7999. Springer, Berlin Heidelberg, pp 130–139 Strickler RE (1973) Change in selected characteristics of students between ninth and twelfth grade as related to high school curriculum Sturtevant N (2012) Benchmarks for grid-based pathfinding. Trans Comput Intell AI Games 4(2):144–148. http://web.cs.du.edu/~sturtevant/papers/benchmarks.pdf Sutcliffe G (2009) The TPTP problem library and associated infrastructure: the FOF and CNF Parts, v3.5.0. J Autom Reason 43(4):337–362 Sutcliffe G, Suttner C (2006) The state of CASC. AI Commun 19(1):35–48 Thrun S (1996) Is learning the n-th thing any easier than learning the first? In: Advances in neural information processing systems, pp 640–646 Thrun S, Pratt L (2012) Learning to learn. Springer, New York Thurstone LL (1938a) Primary mental abilities. Psychometric monographs Thurstone LL (1938b) Primary mental abilities. Psychometric monographs Togelius J, Yannakakis GN, Karakovskiy S, Shaker N (2012) Assessing believability. In: Believable bots, Springer, pp 215–230 Torrey L, Shavlik J (2009) Transfer learning. Handb Res Mach Learn Appl 3:17–35 Turing AM (1950) Computing machinery and intelligence. Mind 59:433–460 Valiant LG (1984) A theory of the learnable. Commun ACM 27(11):1134–1142 Vallati M, Chrpa L, Grzes M, McCluskey TL, Roberts M, Sanner S (2015) The 2014 international planning competition: progress and trends. AI Mag 36(3):90–98 van Rijn JN, Bischl B, Torgo L, Gao B, Umaashankar V, Fischer S, Winter P, Wiswedel B, Berthold MR, Vanschoren J (2013) Openml: a collaborative science platform. In: Machine learning and knowledge discovery in databases. Springer, pp 645–649 Vanschoren J, Blockeel H, Pfahringer B, Holmes G (2012) Experiment databases. Mach Learn 87(2):127–158 Vanschoren J, van Rijn JN, Bischl B, Torgo L (2014) Openml: networked science in machine learning. ACM SIGKDD Explor Newsl 15(2):49–60 Vázquez D, López AM, Marín J, Ponsa D, Gerónimo D (2014) Virtual and real world adaptation for pedestrian detection. IEEE Trans Pattern Anal Mach Intell 36(4):797–809. doi:10.1109/TPAMI.2013.163 Vere SA (1992) A cognitive process shell. Behav Brain Sci 15(03):460–461 von Ahn L (2009) Human computation. In: Design automation conference, 2009. DAC’09. 46th ACM/IEEE, IEEE, pp 418–419 von Ahn L, Blum M, Langford J (2004) Telling humans and computers apart automatically. Commun ACM 47(2):56–60 von Ahn L, Maurer B, McMillen C, Abraham D, Blum M (2008) RECAPTCHA: human-based character recognition via web security measures. Science 321(5895):1465 Wallace CS, Boulton DM (1968) An information measure for classification. Comput J 11(2):185–194 Wallace CS, Dowe DL (1999) Minimum message length and Kolmogorov complexity. Comput J 42(4):270–283 (special issue on Kolmogorov complexity) Wang G, Mohanlal M, Wilson C, Wang X, Metzger M, Zheng H, Zhao BY (2012) Social Turing tests: crowdsourcing sybil detection. arXiv preprint arXiv:1205.3856 Wang P (2010) The evaluation of agi systems. In: Proceedings of the third conference on artificial general intelligence, Citeseer, pp 164–169 Warwick K (2014) Turing Test success marks milestone in computing history. University or Reading Press Release, Wasserman EA, Zentall TR (2006) Comparative cognition: Experimental explorations of animal intelligence. Oxford University Press, Oxford Watkins CJCH, Dayan P (1992) Q-learning. Mach Learn 8(3):279–292 Weiss DJ (2011) Better data from better measurements using computerized adaptive testing. J Methods Meas Soc Sci 2(1):1–27 Weizenbaum J (1966) ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM 9(1):36–45 Wellman M, Reeves D, Lochner K, Vorobeychik Y (2004) Price prediction in a trading agent competition. J Artif Intell Res JAIR 21:19–36 White DR, McDermott J, Castelli M, Manzoni L, Goldman BW, Kronberger G, Jaśkowski W, O’Reilly UM, Luke S (2013) Better GP benchmarks: community survey results and proposals. Genet Program Evolvable Mach 14:3–29. doi:10.1007/s10710-012-9177-2 Whiteson S, Tanner B, White A (2010) The reinforcement learning competitions. AI Mag 31(2):81–94 Whiteson S, Tanner B, Taylor ME, Stone P (2011) Protecting against evaluation overfitting in empirical reinforcement learning. In: 2011 IEEE symposium on adaptive dynamic programming and reinforcement learning (ADPRL), IEEE, pp 120–127 Williams PL, Beer RD (2010) Information dynamics of evolved agents. In: From animals to animats 11, Springer, pp 38–49 Winikoff M, Cranefield S (2014) On the testability of bdi agent systems. J Artif Intell Res JAIR 51:71–131 Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8(7):1341–1390 Wolpert DH (2012) What the no free lunch theorems really mean; how to improve search algorithms. Technical report, Santa fe Institute Working Paper Wolpert DH, Macready WG (1995) No free lunch theorems for search. Technical report SFI-TR-95-02-010 (Santa Fe Institute) Wolpert DH, Macready WG (2005) Coevolutionary free lunches. IEEE Trans Evol Comput 9(6):721–735 Yampolskiy RV (2015) Artificial superintelligence: a futuristic approach. CRC Press, Boca Raton Yonck R (2012) Toward a standard metric of machine intelligence. World Future Rev 4(2):61–70 You J (2015) Beyond the turing test. Science 347(6218):116–116 Zatuchna Z, Bagnall A (2009) Learning mazes with aliasing states: an LCS algorithm with associative perception. Adapt Behav 17(1):28–57 Zhou ZH (2012) Ensemble methods: foundations and algorithms. CRC Press, Boca Raton